#多层感知机的从零开始实现
import torch
from torch import nn
from d2l import torch as d2l

batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)

#实现一个具有单隐藏层的多层感知机，它包含256个隐藏单元
num_inputs, num_outputs, num_hiddens = 784, 10, 256

W1 = nn.Parameter(
    torch.randn(num_inputs, num_hiddens, requires_grad=True) * 0.01)#初始化w1
b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))#初始化b1
W2 = nn.Parameter(#初始化w2
    torch.randn(num_hiddens, num_outputs, requires_grad=True) * 0.01)
b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))#初始化b2

params = [W1, b1, W2, b2]

#实现ReLU激活函数
def relu(X):
    a = torch.zeros_like(X)
    return torch.max(X, a)

#实现我们的模型
def net(X):
    X = X.reshape((-1, num_inputs))#拉成二维矩阵 batchsize 
    H = relu(X @ W1 + b1)
    return (H @ W2 + b2)

loss = nn.CrossEntropyLoss()

#多层感知机的训练过程与softmax回归的训练过程完全相同
num_epochs, lr = 10, 0.1
updater = torch.optim.SGD(params, lr=lr)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)
d2l.plt.show()
#在一些测试数据上应用这个模型
d2l.predict_ch3(net, test_iter)